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2019

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Full-Text Articles in Artificial Intelligence and Robotics

Early Detection Of Fake News On Social Media, Yang Liu Dec 2019

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

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Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni Dec 2019

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

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The Potentials Of Faecal Sludge Treatment Using Local Conditioners In Tanzania: A Review, Doglas Benjamin1 Dec 2019

The Potentials Of Faecal Sludge Treatment Using Local Conditioners In Tanzania: A Review, Doglas Benjamin1

Tanzania Journal of Engineering and Technology (TJET)

Worldwide, every day human beings generate millions of tons of Faecal Sludge (FS), which is rich in water, nutrients, energy, and organic compounds. Yet FS is not being managed in a way that permits us to derive value from its reuse, while at the same time, millions of farmers struggle with depleted soils and lack of water. In most of the developing countries, energy for cooking is mainly derived from cutting of trees, either as wood or charcoal. Resource recovery and reuse from FS can create livelihoods, enhance food security, support green economies, reduce waste and contribute to cost recovery …

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Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger Dec 2019

Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial …

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Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong Dec 2019

Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong

Electrical Engineering Theses and Dissertations

Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.

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Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia Dec 2019

Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia

LSU New Orleans Theses and Dissertations

In this thesis I describe a method where an experience manager chooses actions for non-player characters (NPCs) in intelligent interactive narratives through story graph representation and pruning. The space of all stories can be represented as a story graph where nodes are states and edges are actions. By shaping the domain as a story graph, experience manager decisions can be made by pruning edges. Starting with a full graph, I apply a set of pruning strategies that will allow the narrative to be finishable, NPCs to act believably, and the player to be responsible for how the story unfolds. By …

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Image-Based Malware Classification With Convolutional Neural Networks And Extreme Learning Machines, Mugdha Jain Dec 2019

Image-Based Malware Classification With Convolutional Neural Networks And Extreme Learning Machines, Mugdha Jain

Master's Projects

Research in the field of malware classification often relies on machine learning models that are trained on high level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this research, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or execution of code. Specifically, we visualize malware samples as images and employ image analysis techniques. In this context, we focus on two machine learning models, namely, Convolutional Neural Networks (CNN) and Extreme …

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Hot Fusion Vs Cold Fusion For Malware Detection, Snehal Bichkar Dec 2019

Hot Fusion Vs Cold Fusion For Malware Detection, Snehal Bichkar

Master's Projects

A fundamental problem in malware research consists of malware detection, that is, dis- tinguishing malware samples from benign samples. This problem becomes more challeng- ing when we consider multiple malware families. A typical approach to this multi-family detection problem is to train a machine learning model for each malware family and score each sample against all models. The resulting scores are then used for classification. We refer to this approach as “cold fusion,” since we combine previously-trained models—no retraining of these base models is required when additional malware families are considered. An alternative approach is to train a single model …

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Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur Dec 2019

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

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Ordinal Hyperplane Loss, Bob Vanderheyden Dec 2019

Ordinal Hyperplane Loss, Bob Vanderheyden

Doctor of Data Science and Analytics Dissertations

This research presents the development of a new framework for analyzing ordered class data, commonly called “ordinal class” data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop predictive classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize …

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Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg Dec 2019

Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg

Master's Projects

Inadequate drug experimental data and the use of unlicensed drugs may cause adverse drug reactions, especially in pediatric populations. Every year the U.S. Food and Drug Administration approves human prescription drugs for marketing. The labels associated with these drugs include information about clinical trials and drug response in pediatric population. In order for doctors to make an informed decision about the safety and effectiveness of these drugs for children, there is a need to analyze complex and often unstructured drug labels. In this work, first, an exploratory analysis of drug labels using a Natural Language Processing pipeline is performed. Second, …

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Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten Dec 2019

Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten

Master's Projects

Wildfire damage assessments are important information for first responders, govern- ment agencies, and insurance companies to estimate the cost of damages and to help provide relief to those affected by a wildfire. With the help of Earth Observation satellite technology, determining the burn area extent of a fire can be done with traditional remote sensing methods like Normalized Burn Ratio. Using Very High Resolution satellites can help give even more accurate damage assessments but will come with some tradeoffs; these satellites can provide higher spatial and temporal resolution at the expense of better spectral resolution. As a wildfire burn area …

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Graph Deep Learning: Methods And Applications, Muhan Zhang Dec 2019

Graph Deep Learning: Methods And Applications, Muhan Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone's life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as …

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Finding A Viable Neural Network Architecture For Use With Upper Limb Prosthetics, Maxwell Lavin Dec 2019

Finding A Viable Neural Network Architecture For Use With Upper Limb Prosthetics, Maxwell Lavin

Master of Science in Computer Science Theses

This paper attempts to answer the question of if it’s possible to produce a simple, quick, and accurate neural network for the use in upper-limb prosthetics. Through the implementation of convolutional and artificial neural networks and feature extraction on electromyographic data different possible architectures are examined with regards to processing time, complexity, and accuracy. It is found that the most accurate architecture is a multi-entry categorical cross entropy convolutional neural network with 100% accuracy. The issue is that it is also the slowest method requiring 9 minutes to run. The next best method found was a single-entry binary cross entropy …

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Design Of A Flexible System Simulation Evaluation Framework, Rusheng Ju, Zimin Cai, Wang Song, Wang Peng Dec 2019

Design Of A Flexible System Simulation Evaluation Framework, Rusheng Ju, Zimin Cai, Wang Song, Wang Peng

Journal of System Simulation

Abstract: To meet variable evaluation requirement of complex system simulation, this paper puts forward a design strategy of flexible effectiveness evaluation framework. The composition structure of system simulation evaluation framework is analyzed. To help users design reference dynamically, the method of evaluation reference edit and display is introduced based on Web. To enhance the extensibility of evaluation model, the method of interface design and code generation is investigated. To ensure the flexibility and extensibility of evaluation framework, the relation and mapping mechanism of evaluation references, evaluation model and evaluation result is studied. The framework is realized and verified in …

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Research On Simulation Platform For Equipment System Analysis, Yuping Li, Shaojie Mao, Zhenqi Ju, Zhou Fang, Guoqiang Yan Dec 2019

Research On Simulation Platform For Equipment System Analysis, Yuping Li, Shaojie Mao, Zhenqi Ju, Zhou Fang, Guoqiang Yan

Journal of System Simulation

Abstract: With the development of equipment construction from platform-centric to network-centric, simulation analysis of equipment system is an important means and tool to support the transformation and development of equipment construction under the condition of multi-task joint operation. Starting from the requirement of system simulation analysis, a cloud-based equipment system simulation architecture is proposed to realize flexible and configurable simulation environment according to task requirements; and a high-performance simulation framework is conducted, which provides strong support for different application modes, such as parallel hyper-real-time and distributed simulation deduction. The unified description, organization and management method of model and data resources …

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Research On Corridor Setting Based On Pedestrian Simulation Of Social Groups, Yiting Xu, Zhang Rui Dec 2019

Research On Corridor Setting Based On Pedestrian Simulation Of Social Groups, Yiting Xu, Zhang Rui

Journal of System Simulation

Abstract: As the connector of each space in the hub, the rail transit hub corridor plays the role of transition and buffer. The existence of social groups makes an important impact on pedestrian traffic and its simulation. The paper supplements the consideration of pedestrian traffic related studies on social groups travel, analyses the characteristics of social groups in rail transit hub corridor, improves Moussaïd social group force model, and redevelops AnyLogic micro-simulation platform based on Python language. Taking a subway station in Beijing as an example, fully considering the influence of social groups, it is obtained that the optimal channel …

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Parallel Tasks Optimization Scheduling In Cloud Manufacturing System, Chenwei Feng, Wang Yan Dec 2019

Parallel Tasks Optimization Scheduling In Cloud Manufacturing System, Chenwei Feng, Wang Yan

Journal of System Simulation

Abstract: To solve the problem of unbalanced resource requirements and low resource utilization when the same type of tasks are executed in parallel in the cloud manufacturing system, a task resource scheduling model with the goal of minimizing cost, minimizing time, maximizing reliability and optimizing quality is established. A non-dominated sorting genetic algorithm based on reference points (NSGA-III) is adopted to solve the model by combining real number matrix coding and crossover and mutation based on real number coding instead of common evolutionary strategy. And an optimal decision strategy based on combination of analytic hierarchy process and entropy value method …

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Research On The Value Accessing Method For Calibrating Micro Traffic Simulation Model Parameters, Chenjing Zhou, Rong Jian, Kwok Lam Dec 2019

Research On The Value Accessing Method For Calibrating Micro Traffic Simulation Model Parameters, Chenjing Zhou, Rong Jian, Kwok Lam

Journal of System Simulation

Abstract: Parameter calibration is the precondition of the application of micro traffic simulation technology. This study focuses on the value accessing method for parameter calibration in order to further improve the parameter calibration process. The analysis of the distribution characteristics of each parameter calibration results shows that the parameters have different trends in the process of gradual iteration, and there are multiple optimal solutions for the model parameter calibration results. In this paper, the dispersion is used as the quantitative analysis index of the concentration degree of each parameter calibration result, and the parameter value of the model is determined …

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Research On Source Seeking Methods Of Harmful Gas Leakage In Chemical Industry Parks, Zhao Yong, Bin Chen, Xiaodong Wang, Zhengqiu Zhu, Rongxiao Wang, Xiaogang Qiu Dec 2019

Research On Source Seeking Methods Of Harmful Gas Leakage In Chemical Industry Parks, Zhao Yong, Bin Chen, Xiaodong Wang, Zhengqiu Zhu, Rongxiao Wang, Xiaogang Qiu

Journal of System Simulation

Abstract: Chemical production safety accidents often lead to harmful gas leakage, causing serious environmental damage and casualties. Mastering the information of leaking source quickly can assist in emergency disposal decisions and reduce the harm of accidents. In this paper, Entrotaxis algorithm is used to guide the ground source seeking equipment to move to the vicinity of the leaking source quickly and autonomously in a chemical park scene, and to master the source information. According to the characteristics of the chemical industry park scene, this paper applies intermittent search module into Entrotaxis algorithm and proposes a robust and suitable algorithm (Entrotaxis-Jump …

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Numerical Simulation Analysis Of Anti-Blast Impact Of Underground Rescue Capsule Based On Ls-Dyna, Zhang Fan, Yuanhua Yang, Xiaoxu He, Deng Yu Dec 2019

Numerical Simulation Analysis Of Anti-Blast Impact Of Underground Rescue Capsule Based On Ls-Dyna, Zhang Fan, Yuanhua Yang, Xiaoxu He, Deng Yu

Journal of System Simulation

Abstract: Aiming at the strength problem of the underground rescue cabin under explosion impact load, a finite element model of overall explosion impact load and fluid-solid coupling structure response is established in transient dynamic soft LS-DYNA. The flow field impact load is generated by explosion algorithm, and the propagation of the impact load in the air is calculated. The dynamic response of the rescue cabin structure under the impact load is calculated by the fluid-solid coupling method. The results show that the maximum load occurs on the end surface closest to the explosion source, and the structural deformation is small …

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The Scheduling Algorithm Of Cloud Job Based On Hopfield Neural Network, Yudong Guo, Jinping Zuo Dec 2019

The Scheduling Algorithm Of Cloud Job Based On Hopfield Neural Network, Yudong Guo, Jinping Zuo

Journal of System Simulation

Abstract: Focusing on the low efficiency of cloud job scheduling and the insufficient utility of resource, a job scheduling algorithm based on Hopfield Neural Network is proposed. In order to improve the resource scheduling ability of the system, The resource characteristics which influence the cloud job scheduling are shown. The mathematical model of resource constraints is established, and the Hopfield energy function is designed and optimized. The average utilization rate of 9 nodes is analyzed by using the standard test cases, and the performance and resource utilization of the proposed strategy are compared with three typical algorithms. …

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Simulation Research On Attitude Solution Method Of Micro-Mini Missile, Chunbo Zhao, Junfang Fan, Liu Ning Dec 2019

Simulation Research On Attitude Solution Method Of Micro-Mini Missile, Chunbo Zhao, Junfang Fan, Liu Ning

Journal of System Simulation

Abstract: Aiming at the problem of attitude measurement error in the inertial navigation system of micro and small guided ammunition under eccentric structure, the attitude solution and error compensation optimization are studied by using rotation vector optimization of multiple sub-samples, such as monotone sample, two sub-samples, three sub-samples and four sub-samples, and the fourth-order runge kutta algorithm. Through error compensation and optimization of measured data, the results show that the monomorphic modified algorithm has the worst optimization effect, the fourth-order runge kutta optimization algorithm has the best effect, and the maximum drift error of attitude Angle is better than 10 …

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Multi-Objective Optimization Design Of Aerodynamic Layout For Twin Swept-Wing Aircraft, Yuchang Lei, Dengcheng Zhang, Yanhua Zhang, Guangxu Su, Luo Hao, Zhan Ren Dec 2019

Multi-Objective Optimization Design Of Aerodynamic Layout For Twin Swept-Wing Aircraft, Yuchang Lei, Dengcheng Zhang, Yanhua Zhang, Guangxu Su, Luo Hao, Zhan Ren

Journal of System Simulation

Abstract: Multi-objective optimization of aerodynamic layout is a key technology in the design of vehicles. The overall configuration of the shape parameters is optimized with a double swept-shaped wave shape as the basic configuration. We use NSGA-Ⅱ multi-objective genetic algorithm, take the aircraft double sweep angle as the design variable, consider the maximum takeoff weight, range, volume ratio and other performance indicators, use Elman neural network to establish the relationship between shape parameters and performance parameters, and establish constraints based on mission planning requirements. The Pareto optimal solution set is obtained by using optimized design and the individuals with …

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Fast Simulation Of Yacht In Calm Water Based On Improved Savitsky Method, Xiaochen Li, Yin Yong Dec 2019

Fast Simulation Of Yacht In Calm Water Based On Improved Savitsky Method, Xiaochen Li, Yin Yong

Journal of System Simulation

Abstract: An improved Savitsky method is proposed to increase the accuracy of mathematical model of yacht in fast simulation. Three degrees of freedom mathematical model is established to simulate the motion in displacement regime. warped hull forms of yachts are simplified into prismatic hull and improved Savitsky method is used to calculate resistance. The simulation is carried out in pre-planing regime and planing regime. The validity of the proposed method has been assessed by comparing results with previous experimental data, which shows good agreements. The improved method runs well and the result is accurate when it is added to the …

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Knowledge Representation Method Of Joint Operation Situation Based On Knowledge Graph, Baokui Wang, Wu Lin, Xiaofeng Hu, Xiaoyuan He Dec 2019

Knowledge Representation Method Of Joint Operation Situation Based On Knowledge Graph, Baokui Wang, Wu Lin, Xiaofeng Hu, Xiaoyuan He

Journal of System Simulation

Abstract: Lacking of common knowledge for understanding and judging complex situation of operation by machines is one of the difficulties in intelligent situation cognition. The knowledge representation methods based on knowledge graph are reviewed. The characteristics and difficulties of joint operation situation knowledge representation are analyzed. Moreover, the concept of scenario knowledge graph is presented, as well as the knowledge sources and basic content of scenario knowledge graph are described. This paper also points out that the joint knowledge representation method based on discrete symbols and continuous vectors in specific scenario is an effective way to express joint operation situation …

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Dynamic Collision Optimization Algorithm Based On Ray Detection, Li Xing, Yanfang Fu, Wang Liang, Chengtao Lu Dec 2019

Dynamic Collision Optimization Algorithm Based On Ray Detection, Li Xing, Yanfang Fu, Wang Liang, Chengtao Lu

Journal of System Simulation

Abstract: Aiming at the inaccurate collision detection problem in Unity 3D-based visual simulation systems, a dynamic collision optimization algorithm based on ray detection is designed and implemented. The 3D virtual scene is segmented by octree, which simplifies the detection range of obstacles during the operation of simulation systems. At the same time, based on ray detection, according to the quantization coefficient and distance of the threat degree of obstacles, an appropriate collision collider is dynamically added to the obstacles to complete collision detection. The results show that the algorithm not only improves the fluency of visual simulation systems but also …

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An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza Dec 2019

An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza

Dissertations and Theses

Food wastage is a problem that affects all demographics and regions of the world. Each year, approximately one-third of food produced for human consumption is thrown away. In an effort to track and reduce food waste in the commercial sector, some companies utilize third party devices which collect data to analyze individual contributions to the global problem. These devices track the type of food wasted (such as vegetables, fruit, boneless chicken, pasta) along with the weight. Some devices also allow the user to leave the food in a kitchen container while it is weighed, so the container weight must also …

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Study On Optimization Of Shore Bridge Operator Scheduling Considering The Influence Of Ambient Temperature, Yibin Wang, Haihong Yu, Danlan Xie Dec 2019

Study On Optimization Of Shore Bridge Operator Scheduling Considering The Influence Of Ambient Temperature, Yibin Wang, Haihong Yu, Danlan Xie

Journal of System Simulation

Abstract: The refinement of port management puts forward new requirements for its service capacity, and human factors are the key factors affecting the port service capacity. It is a new and feasible research direction to improve port operation efficiency by adjusting human factors. Based on the theoretical model of temperature affecting efficiency, this paper uses FlexSim system simulation software to analyze the change of employee's working efficiency with time, and studies the optimal scheduling strategy of shore bridge operators under different temperatures. Finally, it is concluded that when the ambient temperature is suitable for human body, the eight-hour scheduling …

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A Method Of Manifold Learning For Locality Preserving Projections Based On Geodesic, Lijun Xu, Jinghan Fang, Yiping Wang Dec 2019

A Method Of Manifold Learning For Locality Preserving Projections Based On Geodesic, Lijun Xu, Jinghan Fang, Yiping Wang

Journal of System Simulation

Abstract: In order to solve the problem of under-fitting state of LPP algorithm in practical application,in this paper, the mapping principle of Locality Preserving Projections (LPP) is discussed in detail. The relationship of LPP method between the under-fitting state on certain dataset and adjacency graph is analyzed. The LPP manifold learning method (ISOLPP) is proposed on the basis of geodesic. The experiment results show that the good embedded effect is achieved by implenmenting ISOLPP method on multiple test data sets. It significantly improves the adaptability of the algorithm by not only inheriting the advantages of LPP algorithm with explicit projection …

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